where \(p(\vec{\vartheta }|H)\)  is the prior—the assumed knowledge of the distributions for the parameters \(\vec{\vartheta }\)  describing the signal, before considering the data. \(p(\vec{d}(t)|\vec{\vartheta },H)\)  is the likelihood—the probability of obtaining the data \(\vec{d}(t)\)  given the model \(H\)  with parameters
\(\vec{\vartheta }\) . The likelihood in a network of \(N\)  detectors is computed as [1]}, [2]}, [3]}
\(p(\vec{d}(t)|\vec{\vartheta },H)= \\\exp \left[ -\frac{1}{2} \sum _{i = 1}^{N} \left<\tilde{d}_{i}(f) - \tilde{s}_{i}(f, \vec{\vartheta })| \tilde{d}_{i}(f) - \tilde{s}_{i}(f, \vec{\vartheta })\right> \right]\) 
